The large quantity of data generated by modern industrial automation systems makes it possible to apply a broad range of plant analytics to the automation systems and processes that make up an industrial enterprise or business. However, access to the industrial data is typically limited to applications and devices that share a common network with the industrial controllers that collect and generate the data. As such, plant personnel wishing to leverage the industrial data generated by their systems in another application are often required to maintain such applications on-site using local resources. Moreover, although a given industrial enterprise may comprise multiple plant facilities at geographically diverse locations (or multiple mobile systems having variable locations), the scope of such applications is limited only to data available on controllers residing on the same local network as the application.
Cloud-based data storage and processing can allow industrial data storage and analytics to be moved from the plant facility to a remote cloud platform. Such cloud-based architectures open the possibility of collective analysis of data from multiple facilities, global access to industrial system performance data, and rapid notification of system issues. Given the large number of data points generated by the many automation systems that make up an industrial enterprise, configuration of cloud-based data collection of these numerous data points can be a time-consuming and labor intensive task.
The above-described deficiencies of current techniques are merely intended to provide an overview of some of the problems of current technology, and are not intended to be exhaustive. Other problems with the state of the art, and corresponding benefits of some of the various non-limiting embodiments described herein, may become further apparent upon review of the following detailed description.